Multimodal: A Text-Image based Cyber-Bullying Detecting with Deep Learning

The practice of categorizing and identifying cyber-bullying behavior, which include using technology to harass or intimidate people—usually through online platforms—is known as cyberbullying detection. To tackle this, we took a look at a dataset that was made public and labeled as bully or non-bully based on text, image, and image-text. Then, we proposed a deep learning model that could identify cyberbullying in multimodal data. Bullying in text is detected using the XLM-RoBERTa with BiGRU model, while bullying in images is identified by the VGG16 pre-trained model. Using attention processes, CLIP, feedback mechanisms, CentralNet, and other tools, we combined these models (VGG16 + XLM-RoBERTa and BiGRU) and developed a model for identifying cyberbullying in image-text based memes.With a respectable accuracy of 72%, our final model demonstrated that the system is capable of identifying the majority of cyberbullying incidents.